Currently, accurate methods of analysis of neuron morphology are based on manual or semi-automated tracing systems. Such tracings can be time consuming and/or are prone to errors in situations where faint or beaded neurites diffusely cover large volumes. This is usually the case with tracing axons of cortical pyramidal neurons, e.g. long range horizontal projections. With this proposal we aim to develop a tool which will automate the reconstruction process of neurites from 3D microscopy stacks of images. The existence of such a tool is critical for advancing neural circuits research. As axons of many neuron types can span the entire brain of an animal (e.g. cortical pyramidal cell axons) or the entire animal itself (e.g. C. elegans), our ultimate goal is to perform reconstructions on a large scale to recover axonal and dendritic arbors of sparsely labeled populations of neurons in their entirety. Our algorithm consists of two main parts. First, a 3D stack of images is segmented into regions based on a local watershed type segmentation procedure. For this, preferred orientations are calculated in each voxel of the thresholded stack of images by applying a bank of steerable 3D Gabor filters. Regions are grown by stepping down in intensity and placing edges between adjacent voxels with dissimilar orientations. Second, created regions are merged into larger structures using global optimization criteria. Here, optimal connecting paths are determined for every pair of regions by maximizing the intensity along the path and, at the same time, keeping the path length to a minimum. Regions are merged depending on the intensity and curvature along their optimally connecting paths. The specific aims of this proposal are as follows. Specific Aim 1: We will develop a graphic user interface (GUI) and optimization based algorithm for the semi-automated tracing of neurites from 3D microscopy stacks of images. The algorithm will be based on the gradient ascent method for finding optimal paths which connect user specified seed points. The GUI will provide the user fast and flexible control over the details of the procedure. We will develop this semi-automated reconstruction tool to function as an autonomous unit, but the GUI and the tracing algorithm are also essential parts of the Specific Aim 2. Specific Aim 2: We will develop a segmentation based algorithm for a fully-automated tracing of neurites from 3D microscopy stacks of images. The algorithm will use watershed type segmentation combined with global optimization based criteria for merging the segmented regions. The fully-automated algorithm will be implemented in the GUI and will utilize methods developed as part of the Specific Aim 1. Specific Aim 3: We will complete the reconstruction process by automatically detecting neuron cell bodies, branching structure, axonal boutons, and dendritic spines. The GUI will provide an opportunity to correct possible errors by connecting and disconnecting branches, removing and adding branches, spines, and boutons. Simple morphometric functions, such as the calculation of length and numbers of boutons and spines, will be implemented as well. Currently, accurate methods of quantitative analysis of neuron morphology and synaptic connectivity are based on manual or semi-automated tracing tools which are time consuming and can be prone to errors. With this proposal we aim to develop a tool that will fully-automate the reconstruction process of neurites from 3D microscopy stacks of images. The existence of such a tool is critical for advancing basic neural circuits research and understanding changes in the central nervous system which underlie its disease state.